Update app.py

app.py

@@ -49,10 +49,9 @@ pipe = pipeline(
 
 
 
-def associate_speakers_with_timestamps(transcription_result, diarization, tolerance=0.1, min_segment_duration=0.1):
+def associate_speakers_with_timestamps(transcription_result, diarization, tolerance=0.05, min_segment_duration=0.1):
     word_segments = transcription_result['chunks']
     diarization_segments = list(diarization.itertracks(yield_label=True))
-    
     speaker_transcription = []
     current_speaker = None
     current_text = []
@@ -62,51 +61,53 @@ def associate_speakers_with_timestamps(transcription_result, diarization, tolera
     def flush_current_segment():
         nonlocal current_speaker, current_text
         if current_speaker and current_text:
-            speaker_transcription.append((current_speaker, ' '.join(current_text)))
+            segment_duration = word_segments[-1]['timestamp'][1] - word_segments[0]['timestamp'][0]
+            if segment_duration >= min_segment_duration:
+                speaker_transcription.append((current_speaker, ' '.join(current_text)))
+            else:
+                unassigned_words.extend([(word['timestamp'][0], word['text']) for word in word_segments])
             current_text = []
 
     for word in word_segments:
         word_start, word_end = word['timestamp']
         word_text = word['text']
-        
         assigned = False
+
         for i in range(last_segment_index, len(diarization_segments)):
             segment, _, speaker = diarization_segments[i]
             if segment.start - tolerance <= word_start < segment.end + tolerance:
                 if speaker != current_speaker:
-                    if current_speaker and len(current_text) == 1 and len(current_text[0].split()) <= 2:
-                        # Si le segment précédent est très court, ne changez pas de locuteur
-                        current_text.append(word_text)
-                    else:
-                        flush_current_segment()
-                        current_speaker = speaker
+                    flush_current_segment()
+                    current_speaker = speaker
                 current_text.append(word_text)
                 last_segment_index = i
                 assigned = True
                 break
-        
+
         if not assigned:
             unassigned_words.append((word_start, word_text))
-    
+
+    flush_current_segment()
+
     # Traitement des mots non assignés
+    unassigned_words.sort(key=lambda x: x[0])  # Trier par timestamp
     for word_start, word_text in unassigned_words:
-        closest_segment = min(diarization_segments, key=lambda x: abs(x[0].start - word_start))
+        closest_segment = min(diarization_segments, key=lambda x: min(abs(x[0].start - word_start), abs(x[0].end - word_start)))
         speaker = closest_segment[2]
         if speaker != current_speaker:
             flush_current_segment()
             current_speaker = speaker
         current_text.append(word_text)
-    
     flush_current_segment()
-    
+
     # Fusion des segments courts
     merged_transcription = []
     for speaker, text in speaker_transcription:
-        if not merged_transcription or merged_transcription[-1][0] != speaker or len(text.split()) > 3:
+        if not merged_transcription or merged_transcription[-1][0] != speaker:
             merged_transcription.append((speaker, text))
         else:
             merged_transcription[-1] = (speaker, merged_transcription[-1][1] + " " + text)
-    
+
     return merged_transcription
     
 def simplify_diarization_output(speaker_transcription):